How advanced data analytics will transform project delivery - - PowerPoint PPT Presentation

How advanced data analytics will transform project delivery

Martin Paver

CEO / Founder www.projectingsuccess.co.uk martinpaver@projectingsuccess.co.uk +44 777 570 4044

Aberdeen Meetup Launch Event

Crossrail

What Happens to the Data?

We take this…..

Lessons Learned Systems

Undertaking a portfolio of work (client projects) improves productivity and reduces costs and schedule uncertainty

And abstract it to this…..

New technologies can have significant and unexpected impact

• n cost and schedule; include for

this in contingencies

2019 Analysis

https://bit.ly/2T7yKnL

“Since 2011 fewer that 25% of oil and gas projects have been delivered on time, projects averaging 10 months delay and coming in around 35%

• ver budget”

Why We Need A Different Approach

Lessons Learned

We’ve been trying it for 40 years Lesson are not learned just repeated Projects are still late and over budget

Learning from Experience Leveraging Experience

The purpose of machine learning But also implies human learning

Fundamentally?

• What is the predisposition of the work to variance?
• Can we predict it?
• How do we test for it?
• How do we treat it and change the future?

Evidence based, tempering against bias.

Project DNA

But we have siloed and unconnected data….

Siloed Data

Data in Separate Tools or Pooled Data?

Tool Driven

Implementation strategy driven by tool selection. Primavera/ASTA, Risk Tool, BIM etc. Considerable tool integration challenge.

Platform Driven

A platform that integrates multiple tools. A one stop shop that integrates database and tools for a project management or BIM centred use case. Vendor lock in.

Data Driven

Connected data is at the core of the solution. Tools and platforms are used to capture, ingest, process, visualise and provide insights. Tool Driven Data Driven Platform Driven

Increasing use of Low Code O365 tools Robotic Process Automation Expert Support for ‘the difficult stuff’

Data Driven

Data Trust: Architecture

Data Trust: Architecture

Project n Project 1

More in the pipeline

Enabling

• rganisations to

securely pool data for the benefit of the collective

Project Analytics Data Trust

Process Automation

Removing Repetitive Processes

71 step process automated.

Removing Repetitive Processes Removing Repetitive Processes

WBS Elements

Scheduling Corpus and Context Extract Triples Adaptive Scheduling Recommendations

Scheduling

Real time update

• f assigned tasks

EVM data Resourcing Weather Supplier performance Dependencies Risks etc

Risk lifecycle

Leveraging Risk Experience

Connected risks Risks-Issues-Lessons Informed risk registers Risk trends Risk mitigations Risk budget

Risks

Systemic Risk A once through process

Stakeholder Management

Or Adaptive, dynamic networks, reflecting real time feedback and historical performance of specific groups/individuals Static Analysis

Bid Analytics

PWin

Aim – to use existing contract data to predict future bid

• utcomes

Data - using ~10,000 contracts from construction industry

• 1. We can predict bid
• utcomes to an

accuracy of 30%.

• 2. This increases where there is a

greater amount of unique data, such as from Network Rail (50%) and North Tyneside (91%)

• 3. Will increase

significantly as bid feedback is added.

What Insights Can be Derived from Data

• Kier is predicted to win the opportunity with 73%.
• Mace are likely to come in 3rd place with a score of 68.6%
• We believe that the prediction can be overturned by:
• Improving BIM score by 6.7%, becoming top quartile.
• Increased focus on sustainability.
• Improved networking with Brian Smith.

Suppliers who bid for more contracts (size of bubble) tend to have a higher average score and lower spread of scores

*Bubble size = number of contracts bid for

Smaller organisations tend to have a lower average score and higher spread

• f scores

A Critical T-Junction

• We accept that our data is patchy
• We acknowledge that its not a priority
• We implement ad hoc improvements
• Data remains an exhaust plume
• Not really ‘invested’
• We believe in the vision
• We develop a roadmap to get there
• We begin to lay the foundations
• We upskill, attend hacks, reshape
• We are ‘invested’

We can’t change this alone

Mobilising a Force for Good

Developing a Uniting Vision: White Paper

Examples of an integrated and united approach

• Scotland data strategy
• BIM

But the data strategy for project delivery is either non existent or disjointed

Why can’t we create something similar to leverage the opportunities within advanced project data analytics? Please join in.

Barriers to Adoption

Its not on the corporate ‘to do’ list

• Lack of a shared vision
• Lack of evidence to support the vision
• Understanding the investment case
• Lack of skilled horsepower
• Lack of data
• Siloed
• Poor quality

Barriers to Adoption

• Robotic Process

Automation

• Data Trusts
• Meetups
• Apprenticeship
• Hackathons
• Masterclasses
• White Paper
• Steering Boards
• Innovation

How Will You Engage With it?

• This is progressing at pace in other sectors
• Project delivery is a late adopter, but ripe for disruption
• The capabilities are being demonstrated on a daily basis
• Some starting small, others more visionary
• When it moves it will be difficult to catch up
• Project management will be transformed

It isn’t hype

Contact

Please find me on Linkedin: Martin Paver

Martin Paver

CEO / Founder www.projectingsuccess.co.uk martinpaver@projectingsuccess.co.uk +44 777 570 4044

Also follow the Project Data Analytics Community

3,201 members 367 members 336 members

Community

Data Trust

Paul Hamer, CEO of Sir Robert McAlpine wrote to 15

• ther CEOs about the creation of a construction

datatrust. A 10 partner innovation proposal was submitted on 30/10/19 to develop the construction data trust. Oil and Gas data trust is out for consultation.

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https://www.marketingtechnews.net/news/ 2019/jan/30/seodata-analyst-hybrid-why- you-should-advocate-data-driven-decision- making-content-driven-field/

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